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Showing 2 results for Chukwuemeka
Chukwuemeka Eneogwe, Ismaila Mohammed Sanni, Alfa Umar Abubakar, Idoko Apeh Abraham,
Volume 9, Issue 2 (Spring 2022)
Abstract
Background: Reservoirs serve as fishing and domestic water resources for the people living around the catchment area. However, natural activities threaten the water quality, therefore, constant and proper monitoring of the reservoir is necessary. This study aimed to examine seasonal variation in water quality parameters of Kubanni reservoir, Zaria, Nigeria.
Methods: Water quality data of Kubanni reservoir, Zaria, Nigeria, for 7 years (January 2014 to December 2020) were collected and analyzed to understand the seasonal variation. Ten water quality parameters including pH, turbidity, electrical conductivity (EC), temperature, total dissolved solids (TDS), dissolved oxygen (DO), chloride (Cl-), total Iron, nitrate (NO3-), and manganese (Mn) were analyzed. The data were analyzed using Kolmogorov-Smirnov test to select the probability distribution which provides the best fit by EasyFit software. The functions included Weibull, Exponential, Fréchet, Gamma, Lognormal, and Normal. Seasonal variation was determined using Spearman’s rank-order correlation.
Results: The results showed that pH, EC, temperature, TDS and NO3- approach the Weibull distribution. Turbidity and total Iron approach the Fréchet distribution. Mn approaches the normal distribution, while DO and Cl- approach the Gamma distribution. The output of non-parametric Spearman’s correlation coefficient and Spearman’s statistical criterion indicates a significant difference at 5% significance level between the pH and total Iron values recorded in both seasons. This suggests that season has an effect on the concentration of pH and total Iron.
Conclusion: Out of the 10 parameters examined, pH and total Iron are climatologically influenced.
Eneogwe Chukwuemeka, Sanni Ismaila Mohammed, Abubakar Alfa Umar, Idoko Apeh Abraham, Bello Abdulrazaq Ayobami,
Volume 9, Issue 4 ( Autumn 2022)
Abstract
Background: Water quality evaluation require arduous laboratory and statistical analyses comprising of sample collection and sometimes transportation to laboratories, which may be expensive. In recent years, there has been an emergent need to monitor the dissolved oxygen (DO) concentrations of Kubanni reservoir as a result of anthropogenic and agricultural pollution. Hence, this study was conducted to apply adaptive neuro-fuzzy inference system (ANFIS)-based modelling in the prediction of DO of Kubanni reservoir.
Methods: Water quality data for seven years were used to develop ANFIS models. Six water quality parameters, namely, total dissolved solids, free carbon dioxide, turbidity, temperature, manganese, and electrical conductivity, were selected for analysis based on their sensitivity. Subtractive clustering and grid partitioning techniques were considered when generating the fuzzy inference system (FIS). Three ANFIS models according to different lengths for training data and testing data were selected for modelling.
Results: The results showed that Model-1 gave the best correlation (R-squared and adjusted R-squared of 0.852503 and 0.845000, respectively) for whole data using six input variables. While Model-3 gave the best correlation (R-squared and adjusted R-squared of 0.807791 and 0.799940, respectively) for whole data using three input variables.
Conclusion: The performance efficiency of ANFIS model 1 using 6 inputs shows that the model is reliable for modelling water quality.
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